Travel Command Generation Device

ABSTRACT

Provided is a travel command generation device that generates, on the basis of an existence probability distribution for a plurality of obstacles, travel commands to avoid collision between the plurality of obstacles and a host vehicle, wherein the speed of collision probability computation is enhanced. On the basis of the existence probability distribution for the plurality of obstacles, a collision probability table is generated, for which a movement distance L on a fixed trajectory and a time T are input and the probabilities of collision between the plurality of obstacles and the host vehicle are output; and on the basis of the collision probability table, travel commands are generated for avoiding collision between the plurality of obstacles and the host vehicle.

TECHNICAL FIELD

The present invention relates to a travel command generation device fora car or a robot and more particularly to a travel command generationdevice that computes a travel command to avoid collision at a highspeed.

BACKGROUND ART

In recent years, in order to improve the safety of cars, a preventivesafety technology has progressed, in which obstacles around a hostvehicle are sensed from sensors such as cameras and radars and a warningis displayed or emergency brake is applied to avoid collision when thepossibility of collision is judged to be high.

In the preventive safety technology, a future position of an obstacle ispredicted to generate a warning display and a collision avoidancecommand in accordance with the prediction and thus, it is important toimprove the prediction accuracy of the future position of the obstacle.

PTL 1 discloses a technology of computing a collision probability inaccordance with a future position prediction in consideration ofstochastic behavior of an obstacle and displaying a warning to a driverwhen the possibility of collision is high.

CITATION LIST Patent Literature

PTL 1: JP 2008-158969 A

SUMMARY OF INVENTION Technical Problem

In the invention disclosed in PTL 1, the probability of collisionbetween an obstacle and a host vehicle is computed supposing that adriver keeps driving on a lane of the host vehicle at substantiallyconstant speed but, in the case of performing more advanced drivingassistance such as automatic travel control, it is necessary to computethe probability of collision between the obstacle and the host vehicleusing a plurality of speed profiles of the host vehicle and perform aniterative computation of searching for the optimum speed profile thatminimizes the collision probability. In this case, a computation timefor a speed command for automatic traveling increases and there is apossibility that the control may not be completed within a certaincontrol cycle.

In view of the above points, it is an object of the present invention torealize a high-speed probability of collision between an obstacle and ahost vehicle in automatic travel control.

Solution to Problem

In order to achieve the above object, the present invention ischaracterized in providing

a travel command generation device that generates, on the basis of anexistence probability distribution for a plurality of obstacles, travelcommands to avoid collision between the plurality of obstacles and ahost vehicle, in which

on the basis of the existence probability distribution for the pluralityof obstacles, a collision probability table is generated, for which amovement distance L on a fixed trajectory and a time T are input and theprobabilities of collision between the plurality of obstacles and thehost vehicle are output and, on the basis of the collision probabilitytable, travel commands are generated to avoid collision between theplurality of obstacles and the host vehicle.

Furthermore, in the present invention, the collision probability tableis generated by, in a case where the movement distance L on the fixedtrajectory and the time T are input, generating a host vehicle area Swhen the host vehicle moves by L on the fixed trajectory and calculatingan integral value, or an average value, or a maximum value within thehost vehicle area S in the existence probability distribution for theplurality of obstacles at the time T.

Furthermore, the present invention searches for a travel command thatminimizes an integrated value or a maximum value in the collisionprobability table during a certain period.

Furthermore, the present invention computes the travel command inparallel by providing a plurality of the collision probability tables.

Advantageous Effects of Invention

According to the present invention, since a computation result of theprobability of collision with an obstacle on the fixed trajectory of thehost vehicle is reused, the speed of the iterative computation of thecollision probability in a search for the optimum travel command of thehost vehicle is enhanced.

Furthermore, according to the present invention, since the capacity ofthe computation result of the collision probability can be made small, ahigher-speed computation can be realized by placing the computationresult of the collision probability in a compact memory capable ofhigh-speed access.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an overall block diagram of a travel command generation deviceaccording to a first embodiment of the present invention.

FIG. 2 is a conceptual diagram of a dynamic map generated by a dynamicmap generation section (10) according to the first embodiment of thepresent invention.

FIG. 3 is a conceptual diagram of prediction of an existence probabilitydistribution for obstacles generated by an obstacle prediction section(11) according to the first embodiment of the present invention.

FIG. 4 is a conceptual diagram of a host vehicle trajectory generated bya trajectory generation section (12) according to the first embodimentof the present invention.

FIG. 5 is a flowchart of a collision probability computation section(13) according to the first embodiment of the present invention.

FIG. 6 is an explanatory diagram fora computation method for a collisionprobability computation step (53) of the flowchart illustrated in FIG.5.

FIG. 7 is a conceptual diagram of a collision probability table outputby the collision probability computation section (13) according to thefirst embodiment of the present invention.

FIG. 8 is a flowchart of a speed command generation section (14)according to the first embodiment of the present invention.

FIG. 9 is a flowchart of a collision probability computation section(13) according to a second embodiment of the present invention.

FIG. 10 is an overall block diagram of a travel command generationdevice according to a third embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

Hereinafter, the configuration and operation of a travel commandgeneration device according to a first embodiment of the presentinvention will be described with reference to FIGS. 1 to 8.

FIG. 1 is an overall block diagram of the travel command generationdevice according to the first embodiment of the present invention.

The travel command generation device (1) accepts input of obstacleinformation from a sensor (7) and host vehicle information from a GPS(8) via a network and computes a command value necessary for travelcontrol on the basis of the input information to output to a vehiclecontrol ECU (9) via a network. The vehicle control ECU (9) drivesvarious actuators (not illustrated) in accordance with the input commandvalue for travel control to control vehicle motion. Note that the sensor(7) in FIG. 1 is specifically a camera, a radar, or the like and isdescribed in terms of notation as unified one. In addition to the GPS(7), the travel command generation device (1) also accepts input of datafrom a sensor (not illustrated) that measures the state of a hostvehicle to control traveling.

The travel command generation device (1) is composed of an IF 1 (4), amap DB (5), a computation unit (2), an external storage section (3), andan IF 2.

The IF 1 (4) is a physical interface for accepting input of the obstacleinformation from the sensor (7) and the host vehicle information fromthe GPS (8) via the network and outputs the input information to thecomputation unit (2). The map DB (5) is a database in which static mapinformation necessary for travel control is saved and outputs necessarymap information to the computation unit (2) in response to a requestfrom the computation unit (2).

The computation unit (2) accepts input of the obstacle information fromthe sensor (7), the host vehicle information from the GPS (8), and thestatic map information from the map DB (5) and, on the basis of theseitems of information, computes the command value necessary for travelcontrol to output a travel command value to the vehicle control ECU (9)via the network.

Specifically, the computation unit (2) is implemented as a semiconductorchip such as a microprocessor or an FPGA.

The computation unit (2) saves intermediate data of a size that cannotbe saved within the computation unit to the external storage section(3). Specifically, the external storage section (3) is implemented as asemiconductor memory such as an SDRAM.

The IF 2 (6) is a physical interface for outputting the travel commandvalue computed by the computation unit (2) via the network.

The computation unit (2) is composed of functions of a dynamic mapgeneration section (10), an obstacle prediction section (11), atrajectory generation section (12), a collision probability computationsection (13), and a speed command generation section (14), and aninternal storage section (15). The respective functions of the dynamicmap generation section (10), the obstacle prediction section (11), thetrajectory generation section (12), the collision probabilitycomputation section (13), and the speed command generation section (14)are implemented as software or hardware.

The dynamic map generation section (10) accepts input of the obstacleinformation from the sensor (7), the host vehicle information from theGPS (8), and the static map information from the map DB (5) andintegrates these items to generate a dynamic map necessary for travelcontrol. FIG. 2 is a conceptual diagram of the dynamic map generated bythe dynamic map generation section (10), illustrating an example of anintersection. By integrating the obstacle information from the sensor(7), the host vehicle information from the GPS (8), and the static mapinformation from the map DB (5), a dynamic database of the position andthe speed of a host vehicle (20), the position of a walkway (24), theposition of a white line (25), the positions and the speeds of obstaclesO1 to 3 (21) to (23) is generated and held in the external storagesection (3).

The obstacle prediction section (11) extracts the obstacle informationfrom the dynamic map generated by the dynamic map generation section(10) and predicts an existence probability distribution for obstacles.FIG. 3 illustrates prediction results of the existence probabilitydistribution using the obstacles O1 to 3 (21) to (23) in the dynamic mapillustrated in FIG. 2 as an example. In a frame of T=0 (s), an initialposition of each obstacle is indicated in accordance with informationfrom sensor data. Next, in a frame of T=1 (s), considering speedinformation from the sensor data and stochastic behaviors, the existenceprobability distribution moves in a velocity direction and diffusesspatially, where respective existence probability distributionscorresponding to the obstacles O1 to 3 (21) to (23) are indicated as(311) to (313). Finally, in a frame of T=2 (s), the existenceprobability distribution moves further in the velocity direction anddiffuses spatially from the frame of T=1 and respective existenceprobability distributions corresponding to the obstacles O1 to 3 (21) to(23) are indicated as (321) to (323). Note that FIG. 3 illustrates theexistence probability distribution for the obstacles in each frame ofT=0, 1, and 3 (s) but, in fact, the existence probability distributionfor the obstacles is generated at a finer time step ΔT, for example, atan interval of ΔT=0.1 (s). Data of the existence probabilitydistribution for the obstacles generated here is saved to the externalstorage section (3) as necessary.

The trajectory generation section (12) extracts the shape of a road andinformation on a route to a destination from the dynamic map generatedby the dynamic map generation section (10) and generates a fixedtrajectory to be passed by the host vehicle in the near future, forexample, within 10 (s). FIG. 4 is an example of the fixed trajectory tobe passed by the host vehicle on the dynamic map illustrated in FIG. 2.FIG. 4 illustrates an example in which the host vehicle turns right anda trajectory (40) passing through near the center of the intersectionand the center of a right lane after the right turn is generated.

The collision probability computation section (13) uses the existenceprobability distribution for the obstacle output by the obstacleprediction section (11) and the fixed route output by the trajectorygeneration section (12) to generate a collision probability table forwhich the movement distance L on the fixed trajectory and the time T areinput and the probability of collision between the obstacle and the hostvehicle is output.

FIG. 5 is a flowchart illustrating processing of the collisionprobability computation section (13).

First, in processing step (50), a variable i for a time loop isinitialized.

Next, in processing step (51), a variable j for a movement distance loopof the host vehicle on the fixed trajectory is initialized.

Next, in processing step (52), the position of the host vehicle on thefixed trajectory is calculated. FIG. 6 illustrates a method ofcalculating the position of the host vehicle on the fixed trajectory. Inthis processing step, the movement distance of the host vehicle on thefixed trajectory is computed first using L=ΔL·j. Next, on the basis ofthe two-dimensional shape of the fixed trajectory, the position of thehost vehicle (x_tmp, y_tmp) at the time of moving by L on the fixedtrajectory is computed.

Next, in processing step (53), the probability of collision between anobstacle and the host vehicle in the case of moving by L=ΔL·j on thefixed trajectory is calculated in a time frame of T=ΔT·i. FIG. 6illustrates a method of calculating the collision probability in a timeframe of T=2. First, a host vehicle area S (60) centered on the positionof the host vehicle (x_tmp, y_tmp) is generated. Next, by integrating acollision probability distribution within the host vehicle area S, thecollision probability is calculated. Note that the collision probabilitymay be computed using a maximum value of the collision probabilitywithin the host vehicle area S or an average value of the collisionprobability within the host vehicle area S.

Next, in processing step (54), the end of the movement distance loop isdetermined. If processing step (52) and processing step (53) areexecuted up to a predetermined movement distance maximum value (ΔL·N),this loop is exited. Otherwise, j is incremented (55) and the processingreturns to processing step (52).

Next, in processing step (55), the end of the time loop is determined.If processing step (52) and processing step (53) are executed up to apredetermined time maximum value (ΔT·M), this loop is exited and allprocessing is ended. Otherwise, i is incremented (57) and the processingreturns to processing step (51).

When the processing illustrated in FIG. 5 is all completed, thecollision probability table P[i][j] for the obstacle and the hostvehicle in a case where the host vehicle moves by L=ΔL·j on the fixedtrajectory at the time ΔT·i (i=0, . . . , M) is generated. FIG. 7illustrates an example of the collision probability table P[i][ ] ( )(i=0, 10, 20). P[i][j] is saved to the internal storage section (15)capable of high-speed access for a high-speed computation of the travelcommand value. Specifically, the internal storage section (15) isimplemented as an SRAM in a semiconductor chip.

FIG. 8 is a flowchart illustrating processing of the speed commandgeneration section (14).

First, in processing step (80), a speed profile to be output isinitialized. The speed profile is a speed command value string extendingfrom the present to the future within a specific period and is saved ina matrix of V[i] (i=0, . . . , M). In addition, a time increment is thesame as that used by the collision probability computation section (13),that is, ΔT.

Next, in processing step (81), the movement distance on the fixed routeextending from the present to the future within a specific period iscomputed. Assuming that the matrix in which the movement distance issaved is L1[i] (i=0, . . . , M), calculation can be performed using thespeed profile as follows.

L1[i+1]=L1[i]+V[i]·ΔT

Next, in processing step (82), a collision probability R for theobstacle and the host vehicle is calculated. The collision probabilityat the time ΔT·i and the movement distance ΔL·j (j=0, . . . , N) iscomputed in advance by the collision probability computation section(13) and maintained as a table in the internal storage section (15),whereby it is possible to compute an ultimate R at high speed by readingcorresponding table data at each time to add.

Next, in processing step (83), whether the collision probability R issmaller than a threshold value Rth is determined. In a case where thecollision probability R is smaller than the threshold value Rth as aresult of the determination, it is determined that no collision with theobstacle occurs when the host vehicle travels using that speed profileand the processing is ended. In a case where the collision probability Ris larger than the threshold value Rth, there is a possibility ofoccurrence of a collision with the obstacle when the host vehicletravels using that speed profile. Accordingly, the speed profile isupdated (84) and the processing returns to processing step (81). Notethat various algorithms used in optimization problems are applied to theupdate of the speed profile in processing step (84), such as sequentialquadratic programming, a genetic algorithm, and an artificial bee colonyalgorithm. With the processing above, the speed profile V[i] (i=0, . . ., M) to avoid collision with an obstacle can be computed at high speed.

Finally, a first value V[0] of the speed profile is selected as a speedcommand value and transmitted to the vehicle control ECU via the networkwithin a specific control cycle.

Hereinafter, the operation of a travel command generation deviceaccording to a second embodiment of the present invention will bedescribed with reference to FIG. 9. Note that the configuration andoperation of the travel command generation device according to thesecond embodiment of the present invention are the same as those of thefirst embodiment except for the operation of a collision probabilitycomputation section (13) and accordingly, the description of the sameportions will be omitted.

FIG. 9 is a flowchart illustrating processing of the collisionprobability computation section (13) according to the second embodimentof the present invention. Note that the processing of the collisionprobability computation section (13) according to the second embodimentof the present invention is the same as that of the first embodimentexcept for processing step (90) and accordingly, the description of thesame portions will be omitted.

In processing step (90), the end of the movement distance loop isdetermined. Since the maximum value of the movement distance of the hostvehicle can be limited depending on the time, the maximum value N of thevariable j of the movement distance loop is calculated so as to beproportional to the time as in N∝k·i. With the processing above, thenumber of times of a computation loop by the collision probabilitycomputation section (13) and the capacity of the collision probabilitytable can be reduced, whereby the speed of the computation can befurther enhanced.

Hereinafter, the configuration of a travel command generation deviceaccording to a third embodiment of the present invention will bedescribed with reference to FIG. 9. Note that the configuration of thetravel command generation device according to the third embodiment ofthe present invention is the same as that of the first embodiment exceptfor speed command generation sections (14 a) and (14 b) and internalstorage sections (15 a) and (15 b) and accordingly, the description ofthe same portions will be omitted.

The collision probability computation section (13) according to thethird embodiment of the present invention generates a plurality ofidentical collision probability tables to save to an internal storagesection 1 (15 a) and an internal storage section 2 (15 b).

Since parallel processing is performed using two sets of the speedcommand generation section 1 (14 a) and the internal storage section 1(15 a), and the speed command generation section 2 (14 b) and theinternal storage section 2 (15 b), it becomes possible to generate thespeed command value at higher speed. Note that, although the degree ofparallelism is set to two in this practical example, the degree ofparallelism may be further raised in order to attain a predeterminedperformance.

REFERENCE SIGNS LIST

-   1 . . . Travel command generation device-   2 . . . Computation unit-   3 . . . External storage section-   4 . . . IF 1-   5 . . . Map DB-   6 . . . IF 2-   7 . . . Sensor-   8 . . . GPS-   9 . . . Vehicle control ECU

1. A travel command generation device that generates, on the basis of anexistence probability distribution for a plurality of obstacles, travelcommands to avoid collision between the plurality of obstacles and ahost vehicle, wherein on the basis of the existence probabilitydistribution for the plurality of obstacles, a collision probabilitytable is generated, for which a movement distance L on a fixedtrajectory and a time T are input and the probabilities of collisionbetween the plurality of obstacles and the host vehicle are output and,on the basis of the collision probability table, travel commands aregenerated to avoid collision between the plurality of obstacles and thehost vehicle.
 2. The travel command generation device according to claim1, wherein in a method of generating the collision probability table,the collision probability table is obtained by, in a case where themovement distance L on the fixed trajectory and the time T are input,generating a host vehicle area S when the host vehicle moves by L on thefixed trajectory and calculating an integral value, or an average value,or a maximum value within the host vehicle area S in the existenceprobability distribution for the plurality of obstacles at the time T.3. The travel command generation device according to claim 2, wherein ina method of generating the travel commands, each travel command isobtained by searching for a travel command that minimizes an integratedvalue or a maximum value in the collision probability table during acertain period.
 4. The travel command generation device according toclaim 3, wherein in a method of generating the travel commands, parallelprocessing is performed by providing a plurality of the collisionprobability tables.
 5. The travel command generation device according toclaim 2, wherein a range of the movement distance L on the fixedtrajectory, which is the input of the collision probability table, issettled depending on the time T.